Integrative analysis workshop with TCGAbiolinks and ELMER
Tiago Chedraoui Silva, Simon Coetzee, Ben Berman, Houtan Noushmehr
2017-07-13
Introduction
Another Bioconductor package we will also be introduce is called ELMER (L. Yao et al. 2015,Chedraoui Silva et al. (2017)) which allows one to identify DNA methylation changes in distal regulatory regions and correlate these signatures with expression of nearby genes to identify transcriptional targets associated with cancer. For these distal probes correlated with a gene, a transcription factor motif analysis is performed followed by expression analysis of transcription factors to infer upstream regulators.
We expect that participants of this workshop will understand the integrative analysis performed by using TCGAbiolinks + ELMER, as well as be able to execute it from the data acquisition process to the final interpretation of the results. The workshop assumes users with an intermediate level of familiarity with R, and basic understanding of tumor biology.
The figure below hihglights the workflow part which will be covered in this section. 
Loading required libraries
library(ELMER)
library(DT)
library(dplyr)ELMER data input
A Multi Assay Experiment object from the MultiAssayExperiment package is the input for multiple main functions of ELMER. To create it you can use the createMAE function.
But instead of using the data downloaded previously we will use one with more samples provided in this package.
library(MultiAssayExperiment)
lusc.exp## class: RangedSummarizedExperiment
## dim: 3742 234
## metadata(0):
## assays(1): ''
## rownames(3742): ENSG00000188984 ENSG00000204518 ...
## ENSG00000162378 ENSG00000036549
## rowData names(2): external_gene_name ensembl_gene_id
## colnames(234): TCGA-22-5472-01A-01R-1635-07
## TCGA-22-5489-01A-01R-1635-07 ... TCGA-56-7730-01A-11R-2125-07
## TCGA-56-7731-01A-11R-2125-07
## colData names(0):lusc.met## class: RangedSummarizedExperiment
## dim: 1702 268
## metadata(0):
## assays(1): ''
## rownames(1702): cg00045114 cg00050294 ... cg13985485 cg08542090
## rowData names(36): addressA addressB ... MASK.extBase MASK.general
## colnames(268): TCGA-43-3394-11A-01D-1551-05
## TCGA-43-3920-11B-01D-1551-05 ... TCGA-98-8022-01A-11D-2245-05
## TCGA-98-8023-01A-11D-2245-05
## colData names(1): samplesAlso we will need to filter the probes in the MAE to select only distal probes.
distal.probes <- get.feature.probe(genome = "hg38", met.platform = "450K")The createMAE function will receive both DNA methylation and gene expression objects. Also filter.probes argument will select only probes in the regions of the distal.probes object.
library(MultiAssayExperiment)
mae <- createMAE(exp = lusc.exp,
met = lusc.met,
save = TRUE,
linearize.exp = TRUE,
filter.probes = distal.probes,
save.filename = "mae_bioc2017.rda",
met.platform = "450K",
genome = "hg19",
TCGA = TRUE)
mae## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] DNA methylation: RangedSummarizedExperiment with 1584 rows and 234 columns
## [2] Gene expression: RangedSummarizedExperiment with 3742 rows and 234 columns
## Features:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample availability DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert ExperimentList into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matricescolData(mae)[1:5,] %>% as.data.frame %>% datatable(options = list(scrollX = TRUE))sampleMap(mae)[1:5,] %>% as.data.frame %>% datatable(options = list(scrollX = TRUE))Analysis
Identifying differentially methylated probes
This step is to identify DNA methylation changes at distal enhancer probes which is carried out by function get.diff.meth.
For each distal probe, the function first rank samples from group 1 and group 2 samples by their DNA methylation beta values. To identify hypomethylated probes, the function compared the lower control quintile (20% of control samples with the lowest methylation) to the lower experiment quintile (20% of experiment samples with the lowest methylation), using an unpaired one-tailed t-test.
Source: Yao, Lijing, et al. “Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.” Genome biology 16.1 (2015): 105.
Main get.diff.meth arguments
| Argument | Description |
|---|---|
| data | A multiAssayExperiment with DNA methylation and Gene Expression data. See createMAE function. |
| diff.dir | A character can be “hypo” or “hyper”, showing differential methylation dirction. It can be “hypo” which is only selecting hypomethylated probes; “hyper” which is only selecting hypermethylated probes |
| cores | A interger which defines the number of cores to be used in parallel process. Default is 1: no parallel process. |
| minSubgroupFrac | A number ranging from 0 to 1,specifying the fraction of extreme samples from group 1 and group 2 that are used to identify the differential DNA methylation. The default is 0.2 because we typically want to be able to detect a specific (possibly unknown) molecular subtype among tumor; these subtypes often make up only a minority of samples, and 20% was chosen as a lower bound for the purposes of statistical power. If you are using pre-defined group labels, such as treated replicates vs. untreated replicated, use a value of 1.0 (Supervised mode) |
| pvalue | A number specifies the significant P value (adjusted P value by BH) cutoff for selecting significant hypo/hyper-methylated probes. Default is 0.01 |
| group.col | A column defining the groups of the sample. You can view the available columns using: colnames(MultiAssayExperiment::colData(data)). |
| group1 | A group from group.col. ELMER will run group1 vs group2. That means, if direction is hyper, get probes hypermethylated in group 1 compared to group 2. |
| group2 | A group from group.col. ELMER will run group1 vs group2. That means, if direction is hyper, get probes hypermethylated in group 1 compared to group 2. |
| test | Statistical test to be used. Options: t.test (DEFAULT), wilcox.test |
| sig.dif | A number specifies the smallest DNA methylation difference as a cutoff for selecting significant hypo/hyper-methylated probes. Default is 0.3. |
| dir.out | A path specify the directory for outputs. Default is is current directory. |
| save | A logic. When TRUE, two getMethdiff.XX.csv files will be generated (see detail) |
sig.diff <- get.diff.meth(data = mae,
group.col = "definition",
group1 = "Primary solid Tumor",
group2 = "Solid Tissue Normal",
minSubgroupFrac = 0.2,
sig.dif = 0.3,
diff.dir = "hypo", # Search for hypomethylated probes in group 1
cores = 1,
dir.out ="result",
pvalue = 0.01)as.data.frame(sig.diff) %>% datatable(options = list(scrollX = TRUE))# get.diff.meth automatically save output files.
# getMethdiff.hypo.probes.csv contains statistics for all the probes.
# getMethdiff.hypo.probes.significant.csv contains only the significant probes which
# is the same with sig.diff
dir(path = "result", pattern = "getMethdiff") ## [1] "getMethdiff.hypo.probes.csv"
## [2] "getMethdiff.hypo.probes.significant.csv"Identifying putative probe-gene pairs
Source: Yao, Lijing, et al. “Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.” Genome biology 16.1 (2015): 105.
nearGenes <- GetNearGenes(data = mae,
probes = sig.diff$probe,
numFlankingGenes = 20, # 10 upstream and 10 dowstream genes
cores = 1)
Hypo.pair <- get.pair(data = mae,
group.col = "definition",
group1 = "Primary solid Tumor",
group2 = "Solid Tissue Normal",
nearGenes = nearGenes,
minSubgroupFrac = 0.4, # % of samples to use in to create groups U/M
permu.dir = "result/permu",
permu.size = 100, # Please set to 100000 to get significant results
pvalue = 0.05,
Pe = 0.01, # Please set to 0.001 to get significant results
filter.probes = TRUE, # See preAssociationProbeFiltering function
filter.percentage = 0.05,
filter.portion = 0.3,
dir.out = "result",
cores = 1,
label = "hypo")Hypo.pair %>% datatable(options = list(scrollX = TRUE))# get.pair automatically save output files.
#getPair.hypo.all.pairs.statistic.csv contains statistics for all the probe-gene pairs.
#getPair.hypo.pairs.significant.csv contains only the significant probes which is
# same with Hypo.pair.
dir(path = "result", pattern = "getPair") ## [1] "getPair.hypo.all.pairs.statistic.csv"
## [2] "getPair.hypo.pairs.significant.csv"
## [3] "getPair.hypo.pairs.significant.withmotif.csv"
## [4] "getPair.hypo.pairs.statistic.with.empirical.pvalue.csv"Motif enrichment analysis on the selected probes
# Identify enriched motif for significantly hypomethylated probes which
# have putative target genes.
enriched.motif <- get.enriched.motif(data = mae,
probes = Hypo.pair$Probe,
dir.out = "result",
label = "hypo",
min.incidence = 10,
lower.OR = 1.1)
names(enriched.motif) # enriched motifs## [1] "ARI3A_HUMAN.H10MO.D" "ARI5B_HUMAN.H10MO.C" "BATF_HUMAN.H10MO.S"
## [4] "BCL6_HUMAN.H10MO.C" "CDX1_HUMAN.H10MO.C" "CDX2_HUMAN.H10MO.C"
## [7] "CEBPA_HUMAN.H10MO.A" "CEBPB_HUMAN.H10MO.A" "CEBPG_HUMAN.H10MO.C"
## [10] "CPEB1_HUMAN.H10MO.D" "DMBX1_HUMAN.H10MO.D" "EVX1_HUMAN.H10MO.D"
## [13] "EVX2_HUMAN.H10MO.A" "FOSB_HUMAN.H10MO.C" "FOSL1_HUMAN.H10MO.A"
## [16] "FOSL2_HUMAN.H10MO.A" "FOS_HUMAN.H10MO.A" "FOXJ3_HUMAN.H10MO.S"
## [19] "FOXO1_HUMAN.H10MO.C" "FOXQ1_HUMAN.H10MO.C" "GATA5_HUMAN.H10MO.D"
## [22] "GSX2_HUMAN.H10MO.D" "HBP1_HUMAN.H10MO.D" "HMGA1_HUMAN.H10MO.D"
## [25] "HMX1_HUMAN.H10MO.D" "HXA10_HUMAN.H10MO.C" "HXA1_HUMAN.H10MO.C"
## [28] "HXB13_HUMAN.H10MO.D" "HXB3_HUMAN.H10MO.D" "HXB7_HUMAN.H10MO.C"
## [31] "HXC6_HUMAN.H10MO.D" "HXD11_HUMAN.H10MO.D" "HXD12_HUMAN.H10MO.D"
## [34] "HXD4_HUMAN.H10MO.D" "HXD8_HUMAN.H10MO.D" "HXD9_HUMAN.H10MO.D"
## [37] "IRF1_HUMAN.H10MO.A" "IRF3_HUMAN.H10MO.C" "IRF5_HUMAN.H10MO.D"
## [40] "IRF9_HUMAN.H10MO.C" "IRX3_HUMAN.H10MO.D" "JUND_HUMAN.H10MO.A"
## [43] "JUN_HUMAN.H10MO.A" "LMX1A_HUMAN.H10MO.D" "MEF2B_HUMAN.H10MO.D"
## [46] "MNX1_HUMAN.H10MO.D" "NANOG_HUMAN.H10MO.A" "NANOG_HUMAN.H10MO.S"
## [49] "NFAC2_HUMAN.H10MO.B" "NFAT5_HUMAN.H10MO.D" "NKX31_HUMAN.H10MO.C"
## [52] "PBX1_HUMAN.H10MO.B" "PO2F1_HUMAN.H10MO.B" "PO3F3_HUMAN.H10MO.D"
## [55] "PO3F4_HUMAN.H10MO.D" "PO4F1_HUMAN.H10MO.D" "PO4F3_HUMAN.H10MO.D"
## [58] "PO5F1_HUMAN.H10MO.A" "PO6F1_HUMAN.H10MO.D" "PROP1_HUMAN.H10MO.D"
## [61] "SOX11_HUMAN.H10MO.D" "SOX1_HUMAN.H10MO.D" "SOX21_HUMAN.H10MO.D"
## [64] "SOX2_HUMAN.H10MO.B" "SOX3_HUMAN.H10MO.D" "SOX8_HUMAN.H10MO.D"
## [67] "SRY_HUMAN.H10MO.B" "STAT2_HUMAN.H10MO.B" "ZFHX3_HUMAN.H10MO.D"
## [70] "ZSC16_HUMAN.H10MO.D"head(enriched.motif["P73_HUMAN.H10MO.A"]) ## probes in the given set that have TP53 motif.## $<NA>
## NULL# get.enriched.motif automatically save output files.
# getMotif.hypo.enriched.motifs.rda contains enriched motifs and the probes with the motif.
# getMotif.hypo.motif.enrichment.csv contains summary of enriched motifs.
dir(path = "result", pattern = "getMotif") ## [1] "getMotif.hypo.enriched.motifs.rda"
## [2] "getMotif.hypo.motif.enrichment.csv"# motif enrichment figure will be automatically generated.
dir(path = "result", pattern = "motif.enrichment.pdf") ## [1] "hypo.all.quality.motif.enrichment.pdf"
## [2] "hypo.quality.A-B.motif.enrichment.pdf"
## [3] "hypo.quality.A-DS.motif.enrichment.pdf"Identifying regulatory TFs
Source: Yao, Lijing, et al. “Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.” Genome biology 16.1 (2015): 105.
## identify regulatory TF for the enriched motifs
TF <- get.TFs(data = mae,
group.col = "definition",
group1 = "Primary solid Tumor",
group2 = "Solid Tissue Normal",
minSubgroupFrac = 0.4,
enriched.motif = enriched.motif,
dir.out = "result",
cores = 1,
label = "hypo")# get.TFs automatically save output files.
# getTF.hypo.TFs.with.motif.pvalue.rda contains statistics for all TF with average
# DNA methylation at sites with the enriched motif.
# getTF.hypo.significant.TFs.with.motif.summary.csv contains only the significant probes.
dir(path = "result", pattern = "getTF") ## [1] "getTF.hypo.significant.TFs.with.motif.summary.csv"
## [2] "getTF.hypo.TFs.with.motif.pvalue.rda"# TF ranking plot based on statistics will be automatically generated.
dir(path = "result/TFrankPlot_family/", pattern = "pdf") ## [1] "ARI3A_HUMAN.H10MO.D.TFrankPlot.pdf"
## [2] "ARI5B_HUMAN.H10MO.C.TFrankPlot.pdf"
## [3] "BATF_HUMAN.H10MO.S.TFrankPlot.pdf"
## [4] "BCL6_HUMAN.H10MO.C.TFrankPlot.pdf"
## [5] "CDX1_HUMAN.H10MO.C.TFrankPlot.pdf"
## [6] "CDX2_HUMAN.H10MO.C.TFrankPlot.pdf"
## [7] "CEBPA_HUMAN.H10MO.A.TFrankPlot.pdf"
## [8] "CEBPB_HUMAN.H10MO.A.TFrankPlot.pdf"
## [9] "CEBPG_HUMAN.H10MO.C.TFrankPlot.pdf"
## [10] "CPEB1_HUMAN.H10MO.D.TFrankPlot.pdf"
## [11] "DMBX1_HUMAN.H10MO.D.TFrankPlot.pdf"
## [12] "EVX1_HUMAN.H10MO.D.TFrankPlot.pdf"
## [13] "EVX2_HUMAN.H10MO.A.TFrankPlot.pdf"
## [14] "FOS_HUMAN.H10MO.A.TFrankPlot.pdf"
## [15] "FOSB_HUMAN.H10MO.C.TFrankPlot.pdf"
## [16] "FOSL1_HUMAN.H10MO.A.TFrankPlot.pdf"
## [17] "FOSL2_HUMAN.H10MO.A.TFrankPlot.pdf"
## [18] "FOXJ3_HUMAN.H10MO.S.TFrankPlot.pdf"
## [19] "FOXO1_HUMAN.H10MO.C.TFrankPlot.pdf"
## [20] "FOXQ1_HUMAN.H10MO.C.TFrankPlot.pdf"
## [21] "GATA5_HUMAN.H10MO.D.TFrankPlot.pdf"
## [22] "GSX2_HUMAN.H10MO.D.TFrankPlot.pdf"
## [23] "HBP1_HUMAN.H10MO.D.TFrankPlot.pdf"
## [24] "HMGA1_HUMAN.H10MO.D.TFrankPlot.pdf"
## [25] "HMX1_HUMAN.H10MO.D.TFrankPlot.pdf"
## [26] "HXA1_HUMAN.H10MO.C.TFrankPlot.pdf"
## [27] "HXA10_HUMAN.H10MO.C.TFrankPlot.pdf"
## [28] "HXB13_HUMAN.H10MO.D.TFrankPlot.pdf"
## [29] "HXB3_HUMAN.H10MO.D.TFrankPlot.pdf"
## [30] "HXB7_HUMAN.H10MO.C.TFrankPlot.pdf"
## [31] "HXC6_HUMAN.H10MO.D.TFrankPlot.pdf"
## [32] "HXD11_HUMAN.H10MO.D.TFrankPlot.pdf"
## [33] "HXD12_HUMAN.H10MO.D.TFrankPlot.pdf"
## [34] "HXD4_HUMAN.H10MO.D.TFrankPlot.pdf"
## [35] "HXD8_HUMAN.H10MO.D.TFrankPlot.pdf"
## [36] "HXD9_HUMAN.H10MO.D.TFrankPlot.pdf"
## [37] "IRF1_HUMAN.H10MO.A.TFrankPlot.pdf"
## [38] "IRF3_HUMAN.H10MO.C.TFrankPlot.pdf"
## [39] "IRF5_HUMAN.H10MO.D.TFrankPlot.pdf"
## [40] "IRF9_HUMAN.H10MO.C.TFrankPlot.pdf"
## [41] "IRX3_HUMAN.H10MO.D.TFrankPlot.pdf"
## [42] "JUN_HUMAN.H10MO.A.TFrankPlot.pdf"
## [43] "JUND_HUMAN.H10MO.A.TFrankPlot.pdf"
## [44] "LMX1A_HUMAN.H10MO.D.TFrankPlot.pdf"
## [45] "MEF2B_HUMAN.H10MO.D.TFrankPlot.pdf"
## [46] "MNX1_HUMAN.H10MO.D.TFrankPlot.pdf"
## [47] "NANOG_HUMAN.H10MO.A.TFrankPlot.pdf"
## [48] "NANOG_HUMAN.H10MO.S.TFrankPlot.pdf"
## [49] "NFAC2_HUMAN.H10MO.B.TFrankPlot.pdf"
## [50] "NFAT5_HUMAN.H10MO.D.TFrankPlot.pdf"
## [51] "NKX31_HUMAN.H10MO.C.TFrankPlot.pdf"
## [52] "PBX1_HUMAN.H10MO.B.TFrankPlot.pdf"
## [53] "PO2F1_HUMAN.H10MO.B.TFrankPlot.pdf"
## [54] "PO3F3_HUMAN.H10MO.D.TFrankPlot.pdf"
## [55] "PO3F4_HUMAN.H10MO.D.TFrankPlot.pdf"
## [56] "PO4F1_HUMAN.H10MO.D.TFrankPlot.pdf"
## [57] "PO4F3_HUMAN.H10MO.D.TFrankPlot.pdf"
## [58] "PO5F1_HUMAN.H10MO.A.TFrankPlot.pdf"
## [59] "PO6F1_HUMAN.H10MO.D.TFrankPlot.pdf"
## [60] "PROP1_HUMAN.H10MO.D.TFrankPlot.pdf"
## [61] "SOX1_HUMAN.H10MO.D.TFrankPlot.pdf"
## [62] "SOX11_HUMAN.H10MO.D.TFrankPlot.pdf"
## [63] "SOX2_HUMAN.H10MO.B.TFrankPlot.pdf"
## [64] "SOX21_HUMAN.H10MO.D.TFrankPlot.pdf"
## [65] "SOX3_HUMAN.H10MO.D.TFrankPlot.pdf"
## [66] "SOX8_HUMAN.H10MO.D.TFrankPlot.pdf"
## [67] "SRY_HUMAN.H10MO.B.TFrankPlot.pdf"
## [68] "STAT2_HUMAN.H10MO.B.TFrankPlot.pdf"
## [69] "ZFHX3_HUMAN.H10MO.D.TFrankPlot.pdf"
## [70] "ZSC16_HUMAN.H10MO.D.TFrankPlot.pdf"Session Info
sessionInfo()## R version 3.4.0 (2017-04-21)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.5
##
## Matrix products: default
## BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] Bioc2017.TCGAbiolinks.ELMER_0.0.0.9000
## [2] SummarizedExperiment_1.6.3
## [3] DelayedArray_0.2.7
## [4] matrixStats_0.52.2
## [5] Biobase_2.36.2
## [6] GenomicRanges_1.28.3
## [7] GenomeInfoDb_1.12.2
## [8] IRanges_2.10.2
## [9] S4Vectors_0.14.3
## [10] BiocGenerics_0.22.0
## [11] TCGAbiolinks_2.5.6
## [12] bindrcpp_0.2
## [13] dplyr_0.7.1
## [14] DT_0.2
## [15] ELMER_2.0.1
## [16] ELMER.data_2.0.1
## [17] MultiAssayExperiment_1.2.1
##
## loaded via a namespace (and not attached):
## [1] rtracklayer_1.36.3 ggthemes_3.4.0
## [3] prabclus_2.2-6 R.methodsS3_1.7.1
## [5] tidyr_0.6.3 ggplot2_2.2.1
## [7] acepack_1.4.1 bit64_0.9-7
## [9] knitr_1.16 aroma.light_3.6.0
## [11] R.utils_2.5.0 data.table_1.10.4
## [13] rpart_4.1-11 hwriter_1.3.2
## [15] RCurl_1.95-4.8 AnnotationFilter_1.0.0
## [17] doParallel_1.0.10 GenomicFeatures_1.28.4
## [19] RSQLite_2.0 commonmark_1.2
## [21] bit_1.1-12 BiocStyle_2.4.0
## [23] xml2_1.1.1 httpuv_1.3.5
## [25] assertthat_0.2.0 viridis_0.4.0
## [27] hms_0.3 evaluate_0.10.1
## [29] BiocInstaller_1.26.0 DEoptimR_1.0-8
## [31] dendextend_1.5.2 km.ci_0.5-2
## [33] DBI_0.7 geneplotter_1.54.0
## [35] htmlwidgets_0.9 reshape_0.8.6
## [37] EDASeq_2.10.0 matlab_1.0.2
## [39] purrr_0.2.2.2 selectr_0.3-1
## [41] ggpubr_0.1.4 backports_1.1.0
## [43] trimcluster_0.1-2 annotate_1.54.0
## [45] biomaRt_2.32.1 ensembldb_2.0.3
## [47] withr_1.0.2 Gviz_1.20.0
## [49] BSgenome_1.44.0 robustbase_0.92-7
## [51] checkmate_1.8.3 GenomicAlignments_1.12.1
## [53] mclust_5.3 mnormt_1.5-5
## [55] cluster_2.0.6 lazyeval_0.2.0
## [57] genefilter_1.58.1 edgeR_3.18.1
## [59] pkgconfig_2.0.1 labeling_0.3
## [61] nlme_3.1-131 ProtGenerics_1.8.0
## [63] nnet_7.3-12 devtools_1.13.2
## [65] bindr_0.1 rlang_0.1.1
## [67] diptest_0.75-7 downloader_0.4
## [69] AnnotationHub_2.8.2 dichromat_2.0-0
## [71] rprojroot_1.2 Matrix_1.2-10
## [73] KMsurv_0.1-5 zoo_1.8-0
## [75] base64enc_0.1-3 whisker_0.3-2
## [77] GlobalOptions_0.0.12 viridisLite_0.2.0
## [79] rjson_0.2.15 bitops_1.0-6
## [81] shinydashboard_0.6.1 R.oo_1.21.0
## [83] ConsensusClusterPlus_1.40.0 Biostrings_2.44.1
## [85] blob_1.1.0 shape_1.4.2
## [87] stringr_1.2.0 ShortRead_1.34.0
## [89] readr_1.1.1 scales_0.4.1
## [91] memoise_1.1.0 magrittr_1.5
## [93] plyr_1.8.4 zlibbioc_1.22.0
## [95] compiler_3.4.0 RColorBrewer_1.1-2
## [97] Rsamtools_1.28.0 XVector_0.16.0
## [99] htmlTable_1.9 Formula_1.2-2
## [101] MASS_7.3-47 stringi_1.1.5
## [103] yaml_2.1.14 locfit_1.5-9.1
## [105] latticeExtra_0.6-28 ggrepel_0.6.5
## [107] survMisc_0.5.4 grid_3.4.0
## [109] VariantAnnotation_1.22.3 tools_3.4.0
## [111] circlize_0.4.0 foreach_1.4.3
## [113] foreign_0.8-69 gridExtra_2.2.1
## [115] digest_0.6.12 shiny_1.0.3
## [117] cmprsk_2.2-7 fpc_2.1-10
## [119] Rcpp_0.12.11 broom_0.4.2
## [121] httr_1.2.1 survminer_0.4.0
## [123] AnnotationDbi_1.38.1 biovizBase_1.24.0
## [125] ComplexHeatmap_1.14.0 psych_1.7.5
## [127] kernlab_0.9-25 colorspace_1.3-2
## [129] rvest_0.3.2 XML_3.98-1.9
## [131] splines_3.4.0 flexmix_2.3-14
## [133] plotly_4.7.0 xtable_1.8-2
## [135] jsonlite_1.5 UpSetR_1.3.3
## [137] modeltools_0.2-21 R6_2.2.2
## [139] Hmisc_4.0-3 htmltools_0.3.6
## [141] mime_0.5 glue_1.1.1
## [143] BiocParallel_1.10.1 DESeq_1.28.0
## [145] class_7.3-14 interactiveDisplayBase_1.14.0
## [147] codetools_0.2-15 mvtnorm_1.0-6
## [149] lattice_0.20-35 tibble_1.3.3
## [151] curl_2.7 survival_2.41-3
## [153] limma_3.32.2 roxygen2_6.0.1
## [155] rmarkdown_1.6 munsell_0.4.3
## [157] GetoptLong_0.1.6 GenomeInfoDbData_0.99.0
## [159] iterators_1.0.8 reshape2_1.4.2
## [161] gtable_0.2.0Bibliography
Chedraoui Silva, Tiago, Simon G. Coetzee, Lijing Yao, Dennis J. Hazelett, Houtan Noushmehr, and Benjamin P. Berman. 2017. “Enhancer Linking by Methylation/Expression Relationships with the R Package Elmer Version 2.” bioRxiv. Cold Spring Harbor Labs Journals. doi:10.1101/148726.
Yao, L, H Shen, PW Laird, PJ Farnham, and BP Berman. 2015. “Inferring Regulatory Element Landscapes and Transcription Factor Networks from Cancer Methylomes.” Genome Biology 16 (1): 105–5.